Large Language Model-Based Financial Intelligence for Automated Compliance,  Risk Analysis, and Regulatory Reporting

Sasidhar Reddy Mondeddula

Large Language Model-Based Financial Intelligence for Automated Compliance, Risk Analysis, and Regulatory Reporting

Authors

Sasidhar Reddy Mondeddula

Keywords:

Anti-Money Laundering (AML), Compliance Automation, Financial Compliance, Financial Decision Support, Financial Intelligence, Governance, Know Your Customer (KYC), Large Language Models (LLMs), Liquidity Risk, Regulatory Reporting, Risk Analytics, Risk Management, Text Generation.

Abstract

The financial intelligence landscape is marked byever-increasing pressure to collect, manage, and utilize data forregulatory compliance, risk decision-making, and reportingobligations. Large Language Models (LLMs) may alleviate the burden imposed by the sheer number of existing financialcontrol obligations as well as regulatory scrutiny

References

[1] Bochkay, K., Brown, S. V., Leone, A. J., & Tucker, J. W. (2023). Textual analysis in accounting: What’s next? Contemporary Accounting Research, 40(2), 765–805.

[2] Mashetty, S. (2023). Leveraging Data Analytics to Enhance Affordable Housing Initiatives and Community Development. Available at SSRN 5249221.

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Published

2024-06-20

How to Cite

Sasidhar Reddy Mondeddula. (2024). Large Language Model-Based Financial Intelligence for Automated Compliance, Risk Analysis, and Regulatory Reporting. Journal of Computational Analysis and Applications (JoCAAA), 33(06), 4222–4227. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5813